def test_cjv():
data = op.join(get_brainweb_1mm_normal(), 'sub-normal01')
wmmask = op.join(get_brainweb_1mm_normal(), 'derivatives',
'volume_fraction_wht.nii.gz')
wmdata = nb.load(wmmask).get_data().astype(np.float32)
gmmask = op.join(get_brainweb_1mm_normal(), 'derivatives',
'volume_fraction_gry.nii.gz')
gmdata = nb.load(gmmask).get_data().astype(np.float32)
ses = 'ses-pn0rf00'
im_file = op.join(data, ses, 'anat', 'sub-normal01_%s_T1w.nii.gz' % ses)
imdata = nb.load(im_file).get_data()
fg_mean = np.mean(imdata[wmdata > .95])
cjvs = []
sigmas = [0.01, 0.03, 0.05, 0.08, 0.12, 0.15, 0.20]
exp_cjvs = [
0.45419429982401677, 0.51149489333538289, 0.60775532593579662,
0.79450797884093927, 1.0781050744254561, 1.3013580100905036,
1.6898180485107017
]
for sigma_n in sigmas:
test_data = imdata + np.random.normal(
0.0, scale=sigma_n * fg_mean, size=imdata.shape)
test_data[test_data < 0] = 0
cjvs.append(cjv(test_data, wmmask=wmdata, gmmask=gmdata))
return np.allclose(cjvs, exp_cjvs, rtol=.01)
def test_cjv(sigma, exp_cjv, rtol=0.1):
data = op.join(get_brainweb_1mm_normal(), 'sub-normal01')
wmmask = op.join(get_brainweb_1mm_normal(), 'derivatives', 'volume_fraction_wht.nii.gz')
wmdata = nb.load(wmmask).get_data().astype(np.float32)
gmmask = op.join(get_brainweb_1mm_normal(), 'derivatives', 'volume_fraction_gry.nii.gz')
gmdata = nb.load(gmmask).get_data().astype(np.float32)
ses = 'ses-pn0rf00'
im_file = op.join(data, ses, 'anat', 'sub-normal01_%s_T1w.nii.gz' % ses)
imdata = nb.load(im_file).get_data()
fg_mean = np.mean(imdata[wmdata > .95])
test_data = imdata + np.random.normal(0.0, scale=sigma*fg_mean, size=imdata.shape)
test_data[test_data < 0] = 0
assert abs(cjv(test_data, wmmask=wmdata, gmmask=gmdata) - exp_cjv) < rtol
def test_cjv(sigma, rtol=0.1):
size = (50, 50)
test_data = np.ones(size)
wmdata = np.zeros(size)
gmdata = np.zeros(size)
gmdata[:, :25] = 1
wmdata[gmdata == 0] = 1
gm_mean = 200
wm_mean = 600
test_data[gmdata > 0] = gm_mean
test_data[wmdata > 0] = wm_mean
test_data[wmdata > .5] += np.random.normal(0.0, scale=sigma*wm_mean, size=test_data[wmdata > .5].shape)
test_data[gmdata > .5] += np.random.normal(0.0, scale=sigma*gm_mean, size=test_data[gmdata > .5].shape)
exp_cjv = sigma * (wm_mean + gm_mean) / (wm_mean - gm_mean)
assert np.isclose(cjv(test_data, wmmask=wmdata, gmmask=gmdata), exp_cjv, rtol=rtol)
def test_cjv(sigma, exp_cjv, rtol=0.1):
data = op.join(get_brainweb_1mm_normal(), 'sub-normal01')
wmmask = op.join(get_brainweb_1mm_normal(), 'derivatives',
'volume_fraction_wht.nii.gz')
wmdata = nb.load(wmmask).get_data().astype(np.float32)
gmmask = op.join(get_brainweb_1mm_normal(), 'derivatives',
'volume_fraction_gry.nii.gz')
gmdata = nb.load(gmmask).get_data().astype(np.float32)
ses = 'ses-pn0rf00'
im_file = op.join(data, ses, 'anat', 'sub-normal01_%s_T1w.nii.gz' % ses)
imdata = nb.load(im_file).get_data()
fg_mean = np.mean(imdata[wmdata > .95])
test_data = imdata + np.random.normal(
0.0, scale=sigma * fg_mean, size=imdata.shape)
test_data[test_data < 0] = 0
assert abs(cjv(test_data, wmmask=wmdata, gmmask=gmdata) - exp_cjv) < rtol
def test_cjv():
data = op.join(get_brainweb_1mm_normal(), 'sub-normal01')
wmmask = op.join(get_brainweb_1mm_normal(), 'derivatives', 'volume_fraction_wht.nii.gz')
wmdata = nb.load(wmmask).get_data().astype(np.float32)
gmmask = op.join(get_brainweb_1mm_normal(), 'derivatives', 'volume_fraction_gry.nii.gz')
gmdata = nb.load(gmmask).get_data().astype(np.float32)
ses = 'ses-pn0rf00'
im_file = op.join(data, ses, 'anat', 'sub-normal01_%s_T1w.nii.gz' % ses)
imdata = nb.load(im_file).get_data()
fg_mean = np.mean(imdata[wmdata > .95])
cjvs = []
sigmas = [0.01, 0.03, 0.05, 0.08, 0.12, 0.15, 0.20]
exp_cjvs = [0.45419429982401677, 0.51149489333538289, 0.60775532593579662, 0.79450797884093927, 1.0781050744254561, 1.3013580100905036, 1.6898180485107017]
for sigma_n in sigmas:
test_data = imdata + np.random.normal(0.0, scale=sigma_n*fg_mean, size=imdata.shape)
test_data[test_data < 0] = 0
cjvs.append(cjv(test_data, wmmask=wmdata, gmmask=gmdata))
return np.allclose(cjvs, exp_cjvs, rtol=.01)
def _run_interface(self, runtime): # pylint: disable=R0914
imnii = nb.load(self.inputs.in_file)
imdata = np.nan_to_num(imnii.get_data())
erode = np.all(
np.array(imnii.get_header().get_zooms()[:3], dtype=np.float32) <
1.2)
# Cast to float32
imdata = imdata.astype(np.float32)
# Remove negative values
imdata[imdata < 0] = 0
# Load image corrected for INU
inudata = np.nan_to_num(nb.load(self.inputs.in_noinu).get_data())
inudata[inudata < 0] = 0
segnii = nb.load(self.inputs.in_segm)
segdata = segnii.get_data().astype(np.uint8)
airdata = nb.load(self.inputs.air_msk).get_data().astype(np.uint8)
artdata = nb.load(self.inputs.artifact_msk).get_data().astype(np.uint8)
headdata = nb.load(self.inputs.head_msk).get_data().astype(np.uint8)
# SNR
snrvals = []
self._results['snr'] = {}
for tlabel in ['csf', 'wm', 'gm']:
snrvals.append(snr(inudata, segdata, fglabel=tlabel, erode=erode))
self._results['snr'][tlabel] = snrvals[-1]
self._results['snr']['total'] = float(np.mean(snrvals))
snrvals = []
self._results['snrd'] = {
tlabel: snr_dietrich(inudata,
segdata,
airdata,
fglabel=tlabel,
erode=erode)
for tlabel in ['csf', 'wm', 'gm']
}
self._results['snrd']['total'] = float(
np.mean([val for _, val in list(self._results['snrd'].items())]))
# CNR
self._results['cnr'] = cnr(inudata, segdata)
# FBER
self._results['fber'] = fber(inudata, headdata)
# EFC
self._results['efc'] = efc(inudata)
# M2WM
self._results['wm2max'] = wm2max(imdata, segdata)
# Artifacts
self._results['qi_1'] = art_qi1(airdata, artdata)
# CJV
self._results['cjv'] = cjv(inudata, seg=segdata)
pvmdata = []
for fname in self.inputs.in_pvms:
pvmdata.append(nb.load(fname).get_data().astype(np.float32))
# FWHM
fwhm = np.array(self.inputs.in_fwhm[:3]) / np.array(
imnii.get_header().get_zooms()[:3])
self._results['fwhm'] = {
'x': float(fwhm[0]),
'y': float(fwhm[1]),
'z': float(fwhm[2]),
'avg': float(np.average(fwhm))
}
# ICVs
self._results['icvs'] = volume_fraction(pvmdata)
# RPVE
self._results['rpve'] = rpve(pvmdata, segdata)
# Summary stats
self._results['summary'] = summary_stats(imdata, pvmdata, airdata)
# Image specs
self._results['size'] = {
'x': int(imdata.shape[0]),
'y': int(imdata.shape[1]),
'z': int(imdata.shape[2])
}
self._results['spacing'] = {
i: float(v)
for i, v in zip(['x', 'y', 'z'],
imnii.get_header().get_zooms()[:3])
}
try:
self._results['size']['t'] = int(imdata.shape[3])
except IndexError:
pass
try:
self._results['spacing']['tr'] = float(
imnii.get_header().get_zooms()[3])
except IndexError:
pass
# Bias
bias = nb.load(self.inputs.in_bias).get_data()[segdata > 0]
self._results['inu'] = {
'range':
float(np.abs(np.percentile(bias, 95.) - np.percentile(bias, 5.))),
'med':
float(np.median(bias))
} #pylint: disable=E1101
mni_tpms = [nb.load(tpm).get_data() for tpm in self.inputs.mni_tpms]
in_tpms = [nb.load(tpm).get_data() for tpm in self.inputs.in_pvms]
overlap = fuzzy_jaccard(in_tpms, mni_tpms)
self._results['tpm_overlap'] = {
'csf': overlap[0],
'gm': overlap[1],
'wm': overlap[2]
}
# Flatten the dictionary
self._results['out_qc'] = _flatten_dict(self._results)
return runtime
def _run_interface(self, runtime): # pylint: disable=R0914,E1101
imnii = nb.load(self.inputs.in_noinu)
erode = np.all(
np.array(imnii.header.get_zooms()[:3], dtype=np.float32) < 1.9)
# Load image corrected for INU
inudata = np.nan_to_num(imnii.get_data())
inudata[inudata < 0] = 0
# Load binary segmentation from FSL FAST
segnii = nb.load(self.inputs.in_segm)
segdata = segnii.get_data().astype(np.uint8)
# Load air, artifacts and head masks
airdata = nb.load(self.inputs.air_msk).get_data().astype(np.uint8)
artdata = nb.load(self.inputs.artifact_msk).get_data().astype(np.uint8)
headdata = nb.load(self.inputs.head_msk).get_data().astype(np.uint8)
rotdata = nb.load(self.inputs.rot_msk).get_data().astype(np.uint8)
# Load Partial Volume Maps (pvms) from FSL FAST
pvmdata = []
for fname in self.inputs.in_pvms:
pvmdata.append(nb.load(fname).get_data().astype(np.float32))
# Summary stats
stats = summary_stats(inudata, pvmdata, airdata, erode=erode)
self._results["summary"] = stats
# SNR
snrvals = []
self._results["snr"] = {}
for tlabel in ["csf", "wm", "gm"]:
snrvals.append(
snr(
stats[tlabel]["median"],
stats[tlabel]["stdv"],
stats[tlabel]["n"],
))
self._results["snr"][tlabel] = snrvals[-1]
self._results["snr"]["total"] = float(np.mean(snrvals))
snrvals = []
self._results["snrd"] = {
tlabel: snr_dietrich(stats[tlabel]["median"], stats["bg"]["mad"])
for tlabel in ["csf", "wm", "gm"]
}
self._results["snrd"]["total"] = float(
np.mean([val for _, val in list(self._results["snrd"].items())]))
# CNR
self._results["cnr"] = cnr(
stats["wm"]["median"],
stats["gm"]["median"],
sqrt(sum(stats[k]["stdv"]**2 for k in ["bg", "gm", "wm"])),
)
# FBER
self._results["fber"] = fber(inudata, headdata, rotdata)
# EFC
self._results["efc"] = efc(inudata, rotdata)
# M2WM
self._results["wm2max"] = wm2max(inudata, stats["wm"]["median"])
# Artifacts
self._results["qi_1"] = art_qi1(airdata, artdata)
# CJV
self._results["cjv"] = cjv(
# mu_wm, mu_gm, sigma_wm, sigma_gm
stats["wm"]["median"],
stats["gm"]["median"],
stats["wm"]["mad"],
stats["gm"]["mad"],
)
# FWHM
fwhm = np.array(self.inputs.in_fwhm[:3]) / np.array(
imnii.header.get_zooms()[:3])
self._results["fwhm"] = {
"x": float(fwhm[0]),
"y": float(fwhm[1]),
"z": float(fwhm[2]),
"avg": float(np.average(fwhm)),
}
# ICVs
self._results["icvs"] = volume_fraction(pvmdata)
# RPVE
self._results["rpve"] = rpve(pvmdata, segdata)
# Image specs
self._results["size"] = {
"x": int(inudata.shape[0]),
"y": int(inudata.shape[1]),
"z": int(inudata.shape[2]),
}
self._results["spacing"] = {
i: float(v)
for i, v in zip(["x", "y", "z"],
imnii.header.get_zooms()[:3])
}
try:
self._results["size"]["t"] = int(inudata.shape[3])
except IndexError:
pass
try:
self._results["spacing"]["tr"] = float(imnii.header.get_zooms()[3])
except IndexError:
pass
# Bias
bias = nb.load(self.inputs.in_bias).get_data()[segdata > 0]
self._results["inu"] = {
"range":
float(np.abs(np.percentile(bias, 95.0) -
np.percentile(bias, 5.0))),
"med":
float(np.median(bias)),
} # pylint: disable=E1101
mni_tpms = [nb.load(tpm).get_data() for tpm in self.inputs.mni_tpms]
in_tpms = [nb.load(tpm).get_data() for tpm in self.inputs.in_pvms]
overlap = fuzzy_jaccard(in_tpms, mni_tpms)
self._results["tpm_overlap"] = {
"csf": overlap[0],
"gm": overlap[1],
"wm": overlap[2],
}
# Flatten the dictionary
self._results["out_qc"] = _flatten_dict(self._results)
return runtime
def make_subject_qc(population, workspace):
print '========================================================================================'
print ''
print ' Tourettome - 006. QUALITY CONTROL '
print ''
print '========================================================================================'
count = 0
for subject in population:
count +=1
print '%s.Running Quality Control for subject %s' %(count, subject)
site_id = subject[0:2]
subdir = os.path.join(workspace, subject)
qcdir = mkdir_path(os.path.join(workspace, subject, 'QUALITY_CONTROL'))
os.chdir(qcdir)
df = pd.DataFrame(index=['%s' % subject])
# EXTRACT ANATOMICAL AND FUNCTIONAL IMAGE QUALITY METRICS
if not os.path.isfile(os.path.join(qcdir, 'quality_paramters.csv')):
############################################################################################
# Anatomical measures
# Load data
anat = nb.load(os.path.join(subdir, 'RAW', 'ANATOMICAL.nii.gz' )).get_data()
anat_mask = nb.load(os.path.join(subdir, 'ANATOMICAL', 'ANATOMICAL_BRAIN_MASK.nii.gz' )).get_data()
anat_gm = nb.load(os.path.join(subdir, 'ANATOMICAL', 'seg_spm/c1ANATOMICAL.nii' )).get_data()
anat_wm = nb.load(os.path.join(subdir, 'ANATOMICAL', 'seg_spm/c2ANATOMICAL.nii' )).get_data()
anat_csf = nb.load(os.path.join(subdir, 'ANATOMICAL', 'seg_spm/c3ANATOMICAL.nii' )).get_data()
# Intermediate measures
anat_fg_mu, anat_fg_sd, anat_fg_size = summary_mask(anat, anat_mask)
anat_gm_mu, anat_gm_sd, anat_gm_size = summary_mask(anat, np.where(anat_gm > 0.5, 1, 0 ))
anat_wm_mu, anat_wm_sd, anat_wm_size = summary_mask(anat, np.where(anat_wm > 0.5, 1, 0 ))
anat_csf_mu, anat_gm_sd, anat_csf_size = summary_mask(anat, np.where(anat_csf > 0.5, 1, 0 ))
anat_bg_data, anat_bg_mask = get_background(anat, anat_mask)
anat_bg_mu, anat_bg_sd, anat_bg_size = summary_mask(anat, anat_bg_mask)
# Calculate spatial anatomical summary measures
df.loc[subject, 'qc_anat_cjv'] = mriqca.cjv(anat_wm_mu, anat_gm_mu, anat_wm_sd, anat_gm_sd)
df.loc[subject, 'qc_anat_cnr'] = mriqca.cnr(anat_wm_mu, anat_gm_mu, anat_bg_sd)
df.loc[subject, 'qc_anat_snr'] = mriqca.snr(anat_fg_mu, anat_fg_sd, anat_fg_size)
df.loc[subject, 'qc_anat_snrd'] = mriqca.snr_dietrich(anat_fg_mu, anat_bg_sd)
df.loc[subject, 'qc_anat_efc'] = mriqca.efc(anat)
df.loc[subject, 'qc_anat_fber'] = mriqca.fber(anat, anat_mask)
# df.loc[subject]['qc_anat_fwhm'] = fwhm(os.path.join(subdir, 'RAW','ANATOMICAL.nii.gz' ),
# os.path.join(subdir, 'ANATOMICAL', 'ANATOMICAL_BRAIN_MASK.nii.gz'),out_vox=False)
############################################################################################
# Functional measures
# Load data
func = np.mean(nb.load(os.path.join(subdir, 'FUNCTIONAL', 'REST_EDIT.nii.gz' )).get_data(), axis =3)
func_mask = nb.load(os.path.join(subdir, 'FUNCTIONAL', 'REST_BRAIN_MASK.nii.gz' )).get_data()
movpar = os.path.join(subdir, 'FUNCTIONAL', 'moco/REST_EDIT_moco2.par')
# Calculate spatial functional summary measures
func_fg_mu, func_fg_sd, func_fg_size = summary_mask(func, func_mask)
df.loc[subject, 'qc_func_snr'] = mriqca.snr(func_fg_mu, func_fg_sd, func_fg_size)
df.loc[subject, 'qc_func_efc'] = mriqca.efc(func)
df.loc[subject, 'qc_func_fber'] = mriqca.fber(func, func_mask)
# df.loc[subject]['qc_func_fwhm'] = fwhm(func, func_mask, out_vox=False)
# Calculate temporal functional summary measures
FD1D = np.loadtxt(calculate_FD_Power(movpar))
frames_in = [frame for frame, val in enumerate(FD1D) if val < 0.2]
quat = int(len(FD1D) / 4)
fd_in_percent = (float(len(frames_in)) / float(len(FD1D))) * 100.
df.loc[subject, 'qc_func_fd'] = str(np.round(np.mean(FD1D), 3))
df.loc[subject, 'qc_func_fd_in'] = str(np.round(fd_in_percent, 2))
df.loc[subject, 'qc_func_fd'] = str(np.round(np.mean(FD1D), 3))
df.loc[subject, 'qc_func_fd_max'] = str(np.round(np.max(FD1D), 3))
df.loc[subject, 'qc_func_fd_q4 '] = str(np.round(np.mean(np.sort(FD1D)[::-1][:quat]), 3))
df.loc[subject, 'qc_func_fd_rms'] = str(np.round(np.sqrt(np.mean(FD1D)), 3))
# Calculate DVARS
func_proc = os.path.join(subdir, 'REGISTRATION', 'REST_EDIT_UNI_BRAIN_MNI2mm.nii.gz')
func_gm = os.path.join(subdir, 'REGISTRATION', 'ANATOMICAL_GM_MNI2mm.nii.gz')
df.loc[subject, 'qc_func_dvars'] = np.mean(np.load(calculate_DVARS(func_proc, func_gm)))
# Calculate TSNR map
if not os.path.isfile(os.path.join(qcdir, 'tsnr.nii.gz')):
tsnr = TSNR()
tsnr.inputs.in_file = os.path.join(subdir, 'FUNCTIONAL', 'REST_EDIT.nii.gz')
tsnr.run()
# os.system('flirt -in tsnr -ref ../ANATOMICAL/ANATOMICAL -applxfm -init ../REGISTRATION/reg_anat/rest2anat_2.mat -out tsnr2anat')
if not os.path.isfile('TSNR_data.npy'):
tsnr_data = nb.load('./tsnr.nii.gz').get_data()
nan_mask = np.logical_not(np.isnan(tsnr_data))
mask = func_mask > 0
data = tsnr_data[np.logical_and(nan_mask, mask)]
np.save(os.path.join(os.getcwd(), 'TSNR_data.npy'), data)
df.loc[subject, 'qc_func_tsnr'] = str(np.round(np.median(np.load('TSNR_data.npy')), 3))
df.to_csv('quality_paramters.csv')
############################################################################################
# Make Image Quality Plots
if not os.path.isfile(os.path.join(qcdir, 'plot_func_tsnr.png')):
# 1. anat brain mask
plot_quality(os.path.join(subdir, 'RAW', 'ANATOMICAL.nii.gz'),
os.path.join(subdir, 'ANATOMICAL', 'ANATOMICAL_BRAIN_MASK.nii.gz'),
subject[0:2], '%s-anat_brain_mask' % subject, 'r', alpha=0.9, title='plot_anat_brain_mask.png')
# 2. anat gm seg
plot_quality(os.path.join(subdir, 'RAW', 'ANATOMICAL.nii.gz'),
os.path.join(subdir, 'ANATOMICAL', 'ANATOMICAL_GM.nii.gz'),
subject[0:2], '%s-anat_gm_seg' % subject, 'r', alpha=0.9, title='plot_anat_gm_seg.png')
# 3. anat2mni
plot_quality(mni_head_1mm, os.path.join(subdir, 'REGISTRATION', 'ANATOMICAL_GM_MNI1mm.nii.gz'),
'MNI', '%s-anat_gm_seg' % subject, 'r', alpha=0.9, title='plot_anat2mni.png',
tissue2=os.path.join(subdir, 'REGISTRATION', 'ANATOMICAL_CSF_MNI1mm.nii.gz'))
# 4. func2mni
plot_quality(os.path.join(subdir, 'REGISTRATION', 'REST_EDIT_MOCO_BRAIN_MEAN_BBR_ANAT1mm.nii.gz'),
os.path.join(subdir, 'ANATOMICAL', 'ANATOMICAL_GM.nii.gz'),
subject[0:2], '%s-func2mni' % subject, 'r', alpha=0.9, title='plot_func2anat.png')
# 5. func_tsnr
plot_quality(os.path.join(subdir, 'QUALITY_CONTROL', 'tsnr.nii.gz'), None,
'TSNR', '%s-func_tsnr' % subject, 'r', alpha=0.9, title='plot_func_tsnr.png')
# 6. plot FD, DVARS, CARPET
resid = nb.load(os.path.join(subdir, 'DENOISE/residuals_compcor/residual_bp_z.nii.gz')).get_data().astype(np.float32)
gm = resid[nb.load(os.path.join(subdir, 'DENOISE/tissue_signals/gm_mask.nii.gz')).get_data().astype('bool')]
wm = resid[nb.load(os.path.join(subdir, 'DENOISE/tissue_signals/wm_mask.nii.gz')).get_data().astype('bool')]
cm = resid[nb.load(os.path.join(subdir, 'DENOISE/tissue_signals/csf_mask.nii.gz')).get_data().astype('bool')]
fd = np.loadtxt(os.path.join(subdir, 'QUALITY_CONTROL/FD.1D'))
dv = np.load(os.path.join(subdir, 'QUALITY_CONTROL/DVARS.npy'))
if not os.path.isfile(os.path.join(qcdir,'xplot_func_motion.png')):
plot_temporal(gm, wm, cm, fd, dv, os.path.join(qcdir,'plot_func_motion.png'))
def _run_interface(self, runtime): # pylint: disable=R0914
imnii = nb.load(self.inputs.in_file)
imdata = np.nan_to_num(imnii.get_data())
erode = np.all(np.array(imnii.get_header().get_zooms()[:3],
dtype=np.float32) < 1.2)
# Cast to float32
imdata = imdata.astype(np.float32)
# Remove negative values
imdata[imdata < 0] = 0
# Load image corrected for INU
inudata = np.nan_to_num(nb.load(self.inputs.in_noinu).get_data())
inudata[inudata < 0] = 0
segnii = nb.load(self.inputs.in_segm)
segdata = segnii.get_data().astype(np.uint8)
airdata = nb.load(self.inputs.air_msk).get_data().astype(np.uint8)
artdata = nb.load(self.inputs.artifact_msk).get_data().astype(np.uint8)
# SNR
snrvals = []
self._results['snr'] = {}
for tlabel in ['csf', 'wm', 'gm']:
snrvals.append(snr(inudata, segdata, airdata, fglabel=tlabel,
erode=erode))
self._results['snr'][tlabel] = snrvals[-1]
self._results['snr']['total'] = float(np.mean(snrvals))
# CNR
self._results['cnr'] = cnr(inudata, segdata)
# FBER
self._results['fber'] = fber(inudata, segdata, airdata)
# EFC
self._results['efc'] = efc(inudata)
# Artifacts
self._results['qi1'] = art_qi1(airdata, artdata)
qi2, bg_plot = art_qi2(imdata, airdata, ncoils=self.inputs.ncoils)
self._results['qi2'] = qi2
self._results['out_noisefit'] = bg_plot
# CJV
self._results['cjv'] = cjv(inudata, seg=segdata)
pvmdata = []
for fname in self.inputs.in_pvms:
pvmdata.append(nb.load(fname).get_data().astype(np.float32))
# ICVs
self._results['icvs'] = volume_fraction(pvmdata)
# RPVE
self._results['rpve'] = rpve(pvmdata, segdata)
# Summary stats
mean, stdv, p95, p05 = summary_stats(imdata, pvmdata)
self._results['summary'] = {'mean': mean, 'stdv': stdv,
'p95': p95, 'p05': p05}
# Image specs
self._results['size'] = {'x': int(imdata.shape[0]),
'y': int(imdata.shape[1]),
'z': int(imdata.shape[2])}
self._results['spacing'] = {
i: float(v) for i, v in zip(
['x', 'y', 'z'], imnii.get_header().get_zooms()[:3])}
try:
self._results['size']['t'] = int(imdata.shape[3])
except IndexError:
pass
try:
self._results['spacing']['tr'] = float(imnii.get_header().get_zooms()[3])
except IndexError:
pass
# Bias
bias = nb.load(self.inputs.in_bias).get_data()[segdata > 0]
self._results['inu'] = {
'range': float(np.abs(np.percentile(bias, 95.) - np.percentile(bias, 5.))),
'med': float(np.median(bias))} #pylint: disable=E1101
# Flatten the dictionary
self._results['out_qc'] = _flatten_dict(self._results)
return runtime
def _run_interface(self, runtime): # pylint: disable=R0914
imnii = nb.load(self.inputs.in_file)
imdata = np.nan_to_num(imnii.get_data())
erode = np.all(np.array(imnii.get_header().get_zooms()[:3], dtype=np.float32) < 1.2)
# Cast to float32
imdata = imdata.astype(np.float32)
# Remove negative values
imdata[imdata < 0] = 0
# Load image corrected for INU
inudata = np.nan_to_num(nb.load(self.inputs.in_noinu).get_data())
inudata[inudata < 0] = 0
segnii = nb.load(self.inputs.in_segm)
segdata = segnii.get_data().astype(np.uint8)
airdata = nb.load(self.inputs.air_msk).get_data().astype(np.uint8)
artdata = nb.load(self.inputs.artifact_msk).get_data().astype(np.uint8)
headdata = nb.load(self.inputs.head_msk).get_data().astype(np.uint8)
# SNR
snrvals = []
self._results["snr"] = {}
for tlabel in ["csf", "wm", "gm"]:
snrvals.append(snr(inudata, segdata, fglabel=tlabel, erode=erode))
self._results["snr"][tlabel] = snrvals[-1]
self._results["snr"]["total"] = float(np.mean(snrvals))
# CNR
self._results["cnr"] = cnr(inudata, segdata)
# FBER
self._results["fber"] = fber(inudata, headdata)
# EFC
self._results["efc"] = efc(inudata)
# M2WM
self._results["wm2max"] = wm2max(imdata, segdata)
# Artifacts
self._results["qi1"] = art_qi1(airdata, artdata)
qi2, bg_plot = art_qi2(imdata, airdata, ncoils=self.inputs.ncoils, erodemask=not self.inputs.testing)
self._results["qi2"] = qi2
self._results["out_noisefit"] = bg_plot
# CJV
self._results["cjv"] = cjv(inudata, seg=segdata)
pvmdata = []
for fname in self.inputs.in_pvms:
pvmdata.append(nb.load(fname).get_data().astype(np.float32))
# ICVs
self._results["icvs"] = volume_fraction(pvmdata)
# RPVE
self._results["rpve"] = rpve(pvmdata, segdata)
# Summary stats
mean, stdv, p95, p05 = summary_stats(imdata, pvmdata)
self._results["summary"] = {"mean": mean, "stdv": stdv, "p95": p95, "p05": p05}
# Image specs
self._results["size"] = {"x": int(imdata.shape[0]), "y": int(imdata.shape[1]), "z": int(imdata.shape[2])}
self._results["spacing"] = {i: float(v) for i, v in zip(["x", "y", "z"], imnii.get_header().get_zooms()[:3])}
try:
self._results["size"]["t"] = int(imdata.shape[3])
except IndexError:
pass
try:
self._results["spacing"]["tr"] = float(imnii.get_header().get_zooms()[3])
except IndexError:
pass
# Bias
bias = nb.load(self.inputs.in_bias).get_data()[segdata > 0]
self._results["inu"] = {
"range": float(np.abs(np.percentile(bias, 95.0) - np.percentile(bias, 5.0))),
"med": float(np.median(bias)),
} # pylint: disable=E1101
mni_tpms = [nb.load(tpm).get_data() for tpm in self.inputs.mni_tpms]
in_tpms = [nb.load(tpm).get_data() for tpm in self.inputs.in_pvms]
overlap = fuzzy_jaccard(in_tpms, mni_tpms)
self._results["tpm_overlap"] = {"csf": overlap[0], "gm": overlap[1], "wm": overlap[2]}
# Flatten the dictionary
self._results["out_qc"] = _flatten_dict(self._results)
return runtime
def _run_interface(self, runtime): # pylint: disable=R0914
imnii = nb.load(self.inputs.in_file)
imdata = np.nan_to_num(imnii.get_data())
erode = np.all(
np.array(imnii.get_header().get_zooms()[:3], dtype=np.float32) <
1.2)
# Cast to float32
imdata = imdata.astype(np.float32)
# Remove negative values
imdata[imdata < 0] = 0
# Load image corrected for INU
inudata = np.nan_to_num(nb.load(self.inputs.in_noinu).get_data())
inudata[inudata < 0] = 0
segnii = nb.load(self.inputs.in_segm)
segdata = segnii.get_data().astype(np.uint8)
airdata = nb.load(self.inputs.air_msk).get_data().astype(np.uint8)
artdata = nb.load(self.inputs.artifact_msk).get_data().astype(np.uint8)
# SNR
snrvals = []
self._results['snr'] = {}
for tlabel in ['csf', 'wm', 'gm']:
snrvals.append(snr(inudata, segdata, fglabel=tlabel, erode=erode))
self._results['snr'][tlabel] = snrvals[-1]
self._results['snr']['total'] = float(np.mean(snrvals))
# CNR
self._results['cnr'] = cnr(inudata, segdata)
# FBER
self._results['fber'] = fber(inudata, segdata, airdata)
# EFC
self._results['efc'] = efc(inudata)
# M2WM
self._results['wm2max'] = wm2max(imdata, segdata)
# Artifacts
self._results['qi1'] = art_qi1(airdata, artdata)
qi2, bg_plot = art_qi2(imdata,
airdata,
ncoils=self.inputs.ncoils,
erodemask=not self.inputs.testing)
self._results['qi2'] = qi2
self._results['out_noisefit'] = bg_plot
# CJV
self._results['cjv'] = cjv(inudata, seg=segdata)
pvmdata = []
for fname in self.inputs.in_pvms:
pvmdata.append(nb.load(fname).get_data().astype(np.float32))
# ICVs
self._results['icvs'] = volume_fraction(pvmdata)
# RPVE
self._results['rpve'] = rpve(pvmdata, segdata)
# Summary stats
mean, stdv, p95, p05 = summary_stats(imdata, pvmdata)
self._results['summary'] = {
'mean': mean,
'stdv': stdv,
'p95': p95,
'p05': p05
}
# Image specs
self._results['size'] = {
'x': int(imdata.shape[0]),
'y': int(imdata.shape[1]),
'z': int(imdata.shape[2])
}
self._results['spacing'] = {
i: float(v)
for i, v in zip(['x', 'y', 'z'],
imnii.get_header().get_zooms()[:3])
}
try:
self._results['size']['t'] = int(imdata.shape[3])
except IndexError:
pass
try:
self._results['spacing']['tr'] = float(
imnii.get_header().get_zooms()[3])
except IndexError:
pass
# Bias
bias = nb.load(self.inputs.in_bias).get_data()[segdata > 0]
self._results['inu'] = {
'range':
float(np.abs(np.percentile(bias, 95.) - np.percentile(bias, 5.))),
'med':
float(np.median(bias))
} #pylint: disable=E1101
# Flatten the dictionary
self._results['out_qc'] = _flatten_dict(self._results)
return runtime
